1. Field of the Invention
The present invention relates to a visual inspection device, a visual inspection method, and a computer program which delete an image of an item determined as a defective item from an image group stored with images of items determined as non-defective items out of a group of images acquired by capturing inspection objects.
2. Description of Related Art
There has hitherto been developed a visual inspection method in which an image acquired by capturing an inspection object is compared with an image of an inspection object to serve as a standard, to thereby determine whether or not the inspection object is a non-defective item. The image to serve as the standard for the determination is an image of an item determined as a non-defective item by visual inspection, and compared with an image acquired by capturing an inspection object to set a determination threshold for making a non-defective/defective determination.
In order to correctly determine a non-defective item as a non-defective item, setting an appropriate determination threshold for making an appropriate non-defective/defective determination is important. For example, Japanese Unexamined Patent Publication No. 2005-265661 discloses an image inspection device using an image processing method of inputting a plurality of non-defective item images to set a determination threshold for making a non-defective/defective determination on an image acquired by capturing an inspection object. In Japanese Unexamined Patent Publication No. 2005-265661, learning is performed each time a non-defective item image is added, and the determination threshold for making the non-defective/defective determination is reset, and hence an appropriate threshold can be set even when slight variations in non-defective/defective determination have occurred.
In the image inspection device using the image processing method disclosed in Japanese Unexamined Patent Publication No. 2005-265661, an erroneous determination might occur in which an item in an image is erroneously determined as a defective item despite it being a non-defective item, and it is thus necessary to execute additional learning which is to add the erroneously determined image to the non-defective item image group and reset the determination threshold. However, there has been a problem in that in some cases, executing additional learning might lead to reduction in determination standard and an image of an item which should essentially be determined as a defective item might be an image erroneously determined as a non-defective item.